Complex plot operations

Here are some strange plot things we have done before

Elizabeth King
Kevin Middleton

Complex plot operations

When at all possible:

  • Generate figures with code
    • Even photos/microscopy/gel panels (annotation_raster())
    • Use version tracking (git)
  • Avoid post-export editing with Illustrator, Canvas, Inkscape, etc.

Reproducible and ultimately time-saving.

Images in a grid

library(jpeg)
img <- readJPEG(system.file("img", "Rlogo.jpg", package="jpeg"))

P <- map(.x = 1:4,
         .f = function(ii) {
           ggplot() +
             annotation_raster(img,
                               xmin = 0, xmax = dim(img)[2],
                               ymin = 0, ymax = dim(img)[1]) +
             xlim(c(0, dim(img)[2])) +
             ylim(c(0, dim(img)[1])) +
             coord_equal() +
             ggpubr::theme_transparent()
         })

wrap_plots(P) +
    plot_annotation(tag_levels = 'a', tag_suffix = ".")

Images in a grid

Resizing plots in a grid

  • patchwork automatically handles axis alignment
  • cowplot::plot_grid() gives fine control over relative plot size

Data to work with

set.seed(87534579)

M <- tibble(x1 = runif(20),
            x2 = runif(20),
            y1 = rnorm(20),
            y2 = rnorm(20))
p1 <- ggplot(M, aes(x1, y1)) + geom_point()
p2 <- ggplot(M, aes(x2, y1)) + geom_point()
p3 <- ggplot(M, aes(x1, y2)) + geom_point()
p4 <- ggplot(M, aes(x2, y2)) + geom_point()

plot_grid(p1, p2, p3, p4)

Data to work with

Removing axes

p1 <- ggplot(M, aes(x1, y1)) + geom_point() +
  theme(axis.title.x = element_blank(), axis.text.x = element_blank())
p2 <- ggplot(M, aes(x2, y1)) + geom_point() +
    theme(axis.title = element_blank(), axis.text = element_blank())
p3 <- ggplot(M, aes(x1, y2)) + geom_point()
p4 <- ggplot(M, aes(x2, y2)) + geom_point() +
  theme(axis.title.y = element_blank(), axis.text.y = element_blank())

plot_grid(p1, p2, p3, p4)

Removing axes

Aligning axes

plot_grid(p1, p2, p3, p4,
          align = "hv")

Aligning axes

Resizing sublots

plot_grid(p1, p2, p3, p4,
          rel_widths = c(1.05, 1),
          rel_heights = c(1, 1.10))

Resizing sublots

Moving the legend around

  • theme(legend.position = ...) for basic positioning
  • lemon package for more options

Legend positioning 1

p <- ggplot(palmerpenguins::penguins |> 
              drop_na(bill_length_mm, bill_depth_mm),
            aes(bill_length_mm, bill_depth_mm, color = species)) +
  geom_point() +
  facet_wrap("species", nrow = 2)
p +
  theme(legend.position = "inside",
        legend.position.inside = c(0.7, 0.1))

Legend positioning 1

Legend positioning with reposition_legend

  • Can span multiple grid positions
reposition_legend(p, "center", panel = "panel-2-2")

Legend positioning with reposition_legend

Legend positioning 2

  • Extracting the legend and adding manually with grid.arrange()
p1 <- ggplot(M, aes(x1, y1, color = y1)) + geom_point()
p2 <- ggplot(M, aes(x2, y1, color = y1)) + geom_point()
p3 <- ggplot(M, aes(x1, y2, color = y1)) + geom_point()

library(gridExtra)
legend <- g_legend(p1 + theme(legend.position = "bottom"))
grid.arrange(p1 + theme(legend.position = "hidden"),
             p2 + theme(legend.position = "hidden"),
             p3 + theme(legend.position = "hidden"),
             legend)

Legend positioning 2

Customizing the legend elements

  • guides() and guide_legend()
  • Control fonts, position, layout of legend elements
ggplot(bp, aes(x = Duration, y = Prop_removed, color = Bee_type)) +
  geom_path(linewidth = 3, alpha = 0.5, show.legend = FALSE) +
  geom_point(size = 10, alpha = 0.5) +
  guides(color = guide_legend(override.aes = list(size = 3,
                                                  alpha = 1)))

Customizing the legend elements

Customizing facets

PP <- palmerpenguins::penguins |> 
  mutate(Penguin_label = paste0(species, "\npenguin"))

ggplot(PP |> drop_na(bill_length_mm, bill_depth_mm),
       aes(bill_length_mm, bill_depth_mm, color = species)) +
  geom_point(show.legend = FALSE) +
  facet_grid(. ~ Penguin_label) +
  cowplot::theme_cowplot(font_size = 10) +
  theme(strip.background = element_blank(),
        strip.text = element_text(face = "bold", size = rel(1.5)))

Customizing facets